Downloads · 30 days
103
2% of all-time downloads
t5-address-standardizer
Hnabil/t5-address-standardizer
t5-address-standardizer is a machine learning model from Hnabil. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This model is t5-base fine-tuned to transform incorrect and non-standard addresses into standardized addresses. , primarily trained for US addresses.
All-time downloads
6K
Public
Parameters
223M
1.8 GB on disk
Likes
11
Public
Repo makeup
Click a slice to open those files.
.bin892 MB · 50%
At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- Jun 15, 2023
- Updated
- Nov 10, 2024
- SHA
- 029b6443
Try a prompt
Model card
From the Hugging Face model README
Address Standardization and Correction Model
This model is t5-base fine-tuned to transform incorrect and non-standard addresses into standardized addresses. , primarily trained for US addresses.
How to use the model
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("Hnabil/t5-address-standardizer")
tokenizer = AutoTokenizer.from_pretrained("Hnabil/t5-address-standardizer")
inputs = tokenizer(
"220, soyth rhodeisland aveune, mason city, iowa, 50401, us",
return_tensors="pt"
)
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['220, s rhode island ave, mason city, ia, 50401, us']
Training data
The model has been trained on data from openaddresses.io.
- Library
- transformers
- Type
- t5
- Created
- Jun 15, 2023
- Updated
- Nov 10, 2024